code: def test_e2e_detect(samples_source, fridge_class_map, backbone):
      img_path = samples_source / "fridge/odFridgeObjects/images/10.jpg"
      tfms_ = tfms.A.Adapter([A.Resize(384, 384), A.Normalize()])
      model = models.ultralytics.yolov5.model(
          num_classes=5,
          img_size=384,
          backbone=backbone(pretrained=True),
      )
      pred_dict = models.ultralytics.yolov5.end2end_detect(
          img_path, tfms_, model, fridge_class_map
      )
      assert len(pred_dict["detection"]["bboxes"]) == 0
  
file_path: icevision/tests/models/ultralytics/yolov5/test_prediction.py
imports: import pytest
  from icevision.all import *
  from icevision.models.ultralytics.yolov5.backbones import *
  import albumentations
  from icevision.models.inference_sahi import IceSahiModel
  from sahi.prediction import PredictionResult
label_desc: Use image node_id 1 for calling a global function with example usage: test_e2e_detect(samples_source, fridge_class_map, backbone) without return types